The Truth About Flow Cytometry Measurement Compensation

In most research labs, there exists a notebook that contains the tried and true protocols for lab members to follow. These hallowed, often coffee-stained, pages teach the researchers everything — from how to make media, passage cells, and run restriction digestions, to how to prepare cells for flow cytometry analysis. These protocols are time-honored and tested, so the new researcher doesn’t question the wisdom of the “Protocols Book”.

Unfortunately, these pages are not refreshed with the best practices that have evolved over time as the technology and our understanding has changed and grown. The “truths” in this book are not always right anymore, but the new user doesn’t necessarily know any differently. It is for this reason that there are suboptimal practices that permeate flow cytometry experiments to this day. The last 2 blog articles have discussed the theory and practice of compensation. This blog article will help shine light on some of these historical practices and why they need to be changed.

You can use a universal negative

The idea behind the Universal Negative is that a single tube, typically unstained cells, is used to set the negative population for establishing the compensation matrix. This was the default method when performing manual compensation.

As discussed previously, the Universal Negative violates the 2^nd rule of Compensation, which states the positive and negative carrier must have the same background.

A lot of the automated analysis packages on flow cytometry software, both acquisition and analysis, offer the ability to identify a single sample that is supposed to be representative of the background fluorescence of the population. As shown in the figure below, it is clear that a single, unstained sample cannot be used to properly set the background.

Figure 1: Unstained cells (red) and beads (blue) have different background fluorescence.

For those using BD DIVA software to acquire samples when setting up compensation, make sure to uncheck “Include separate unstained control tube/well”. After acquiring each compensation control, after gating on the FCSxSSC plot (P1), the histogram in question will have a P2 gate that is placed around the positive population. The user can draw a P3 gate around the negative population. The software will now use the P3 gate to calculate compensation.

Compensation values cannot be over a certain percentage.

Every now and then, there is a suggestion that compensation must be no greater than some value, usually around the 40 to 50% range. It’s important to remember that compensation is the result of a mathematical correction based on the appropriate controls, as described earlier in this series.

This is often followed by the idea that, rather than have the compensation value too high, researchers should adjust the voltage to reduce the compensation value. As shown in Figure 2, while changing the voltage does impact the compensation value, it does not impact the spread of the data.

Figure 2: Spreading error is independent of compensation value. PE and PE-Cy5 were collected over a range of voltages for the PE-Cy5 detector while holding the PE detector voltage constant. Compensation values for each voltage were calculated in FlowJo, yielding values ranging from 2.7% up to 2,900%. Importantly, the spread of the PE-Cy5 beads in the PE channel, as indicated by the dashed line, is unchanged. This data shows that a high compensation value is not indicative of severe spillover spreading. Data courtesy of the University of Wisconsin Carbone Cancer Center Flow Cytometry Lab.

The best practice to properly set voltages is during panel optimization, to optimize the voltage by performing a voltage walk (also known as a voltration). In this process, properly titered antibodies are used to stain cells, which are run at increasing voltages. The Staining Index is calculated and the point where the best separation for the antibody is identified.

Figure 3: Voltage walk (Voltration) with 2 different antibodies. On the left, the optimal voltage (in green) is the same as determined by the peak 2 method. On the right, increasing the voltage increases the SI by approximately 15%.

Compensation introduces error into your dataset.

Error is present in all scientific measurements. This comes from various sources, from pipetting error to photon counting. This error ends up in the data, leading to the spread of the data that is observed in flow cytometry plots.

One thing that worries researchers when they compensate is that there is a large error being introduced into the dataset. This is simply not true and is the result of how the data is displayed and the log scale, as illustrated in Figure 4.

Figure 4: Moving from the high end to the low end of the log scale impacts the perception of the data.

The 5% and 95% were determined in the same channel (red arrow), which allows the spread of the data to be determined (blue arrows). When properly compensated, the error (872 units) must be maintained. However, the data is now shifted to the low end of the log scale (right plot).

This is why new visualization methods are needed, to help see the full spread of the data. As shown in Figure 5, there is a large amount of data on the axis (red circle). To properly visualize that, and the spread of the data, a transformation has been applied, in this case the Bi-Exponential transformation. This allows for the full spread of the data to be visualized, and proper gating to be established.

Figure 5: Biexponential transformation to properly visualize the spread of the data.

You can reuse your compensation matrix.

There are those days when everything goes wrong. The experiment is salvaged, but the controls are lost. “No problem,” thinks the researcher, “the matrix from last week should be find, right?”

Wrong! The idea of reusing the matrix from a previous experiment is one that people cling to, but is not good science.

For compensation to be accurate, the third rule states that it must be an identical fluorochrome and identical settings. Using a matrix from last week (or even yesterday) can easily violate that rule. Tandems degrade and instruments can vary. What if the person before ran a dye that sticks, and compromises your data? What if the instrument had a major alignment or repair?

Bottom line, with the relative low cost of capture beads, and the fact that you don’t need to use the same concentration of antibody as on your samples, there is no excuse to reuse a matrix or ignore this critical control.

The topic of compensation is a critical one for the cytometrist to understand. It requires adherence to some specific rules, an understanding of how the instrument works, and how fluorescence occurs. Poor or incorrect compensation can easily lead to incorrect conclusions, and decreases the reliability and robustness of the data generated.

Understanding compensation, and being armed with the knowledge, allows the researcher to combat those fairytales that continue to make their rounds in science. It is time to put them to bed and move forward with a full understanding of the process.

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